{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.4.1 dataframe.plot功能\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.4.2 单样本：生成随机数时间序列 -> 可视化数据样本 -> 模拟处理、绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = np.random.randn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        1.74493096e+00,  3.73051604e-01, -7.72345971e-01, -5.14471316e-01,\n",
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       "       -1.11629187e+00,  4.99765504e-01,  8.95356172e-01,  1.05301684e+00,\n",
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       "       -1.96974263e+00,  8.85565923e-01,  7.53483741e-01,  5.53675798e-01,\n",
       "        1.93428055e-01,  4.73600078e-01,  7.37908746e-01, -8.52034017e-01,\n",
       "        1.15976114e-01,  3.92228214e-02, -2.17880702e-01, -5.74905610e-01,\n",
       "        4.89060147e-01, -6.17916248e-01,  7.66799316e-01,  5.87615813e-01,\n",
       "       -8.88204250e-01,  6.95983845e-01,  1.55567694e+00,  1.17021264e+00,\n",
       "        5.02399578e-01, -6.81347905e-02, -2.91946424e+00, -1.07252208e+00,\n",
       "        4.48779881e-01, -4.05001767e-01,  1.39651053e+00, -1.43737613e-01,\n",
       "       -1.55662088e+00,  2.68574396e-01, -9.89241639e-01,  8.96629635e-01,\n",
       "        4.75055931e-01, -1.38838122e+00,  9.55892184e-01, -3.54504310e-01,\n",
       "        6.67657251e-01,  3.96623705e-01, -1.35131007e+00, -8.94538780e-01,\n",
       "       -7.08457133e-01,  1.31664742e-01,  2.43098970e+00,  1.88952592e-01,\n",
       "       -8.91494163e-01, -4.31578161e-01,  6.31571081e-03,  1.61919648e-01,\n",
       "       -1.30161755e+00, -2.07632180e+00, -1.88899314e-01,  1.52233420e+00,\n",
       "        9.54438004e-01,  5.30642650e-01, -4.27630415e-01,  2.21003405e+00,\n",
       "       -2.53405788e+00,  1.05245783e+00, -1.52730942e+00, -1.00631983e-01,\n",
       "        3.30825428e-02,  5.22479603e-01, -1.16281298e+00, -1.06823525e+00,\n",
       "        1.56894274e-01,  2.62678194e-02,  9.10194085e-01, -1.27618876e+00,\n",
       "       -2.09911620e-01,  1.52412987e+00, -1.26731566e-01, -3.14919883e+00,\n",
       "       -1.61235878e+00,  2.68037480e-01, -6.28780152e-01, -7.14352277e-01,\n",
       "        3.38817320e-01, -1.65022397e+00, -1.00831720e+00, -1.13143866e+00,\n",
       "        3.19989475e-01,  1.00690425e+00, -3.08797252e-01, -4.29685568e-01,\n",
       "       -1.32017322e+00,  3.67302481e-01,  5.67879530e-01, -1.52242485e+00,\n",
       "       -1.42389509e-01, -1.18947273e-01, -1.03342052e+00, -1.69545615e-01,\n",
       "        3.28879115e-01, -4.79508730e-02, -1.45099820e+00, -1.59555493e+00,\n",
       "       -1.44301437e-01, -1.04114715e+00, -5.30853265e-01, -2.36885266e-01,\n",
       "       -1.10413868e+00,  4.30820895e-01, -8.98594776e-01,  5.40332064e-01,\n",
       "       -4.32492588e-01,  3.79652209e-01,  1.67811514e+00, -1.26159952e+00,\n",
       "       -4.45706184e-01, -4.99791661e-01, -3.85539339e-01, -1.63650537e-01,\n",
       "       -9.68211415e-01, -3.71383588e-01, -6.88946097e-01,  8.08487341e-01,\n",
       "       -7.49740694e-02,  1.11029440e-01,  1.69966055e-01,  4.32201414e-01,\n",
       "        1.37261475e+00,  8.73790989e-01,  9.06393010e-01,  1.12391188e+00,\n",
       "        8.43333750e-01,  1.14917825e+00, -3.09688697e-01,  6.51394222e-01,\n",
       "       -3.35089568e-01,  4.34595477e-01,  4.04680432e-01,  3.73194691e-02,\n",
       "        1.00703787e-01, -3.39359103e-02, -1.40224994e+00,  3.25473158e-01,\n",
       "        8.27252546e-01, -7.77282915e-01, -3.82362980e-01,  7.35471331e-01,\n",
       "        6.14993288e-01,  6.23467507e-01,  1.44358613e+00, -8.92524166e-01,\n",
       "       -3.63113648e-02,  8.09634943e-01,  5.58639522e-01,  1.65329050e+00,\n",
       "       -8.09371652e-01, -2.40802924e-01,  8.39554009e-01, -6.94028801e-01,\n",
       "       -1.27319772e+00,  6.93391904e-01, -1.46783424e+00, -1.72777178e-01,\n",
       "        5.51917145e-01,  1.67270463e+00,  6.44086798e-01, -2.45704474e-01,\n",
       "       -1.85795318e-01, -1.90315488e+00,  1.51253745e+00,  1.09356742e+00,\n",
       "       -1.66020841e-01,  1.85430825e+00,  6.21557373e-01,  5.57727596e-01,\n",
       "        2.35529213e-02, -9.82390091e-01, -3.16657269e-01,  5.99540922e-01,\n",
       "        9.45560016e-01, -9.80250923e-02, -1.27803221e+00,  2.54054768e-01,\n",
       "       -7.22895835e-01,  7.22830918e-01, -5.10234878e-01,  1.87544776e-01,\n",
       "       -8.55817944e-01,  1.24123550e+00,  1.07520920e+00, -2.90261577e-02])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = np.random.randn(1000)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'plot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-848b80e64df8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'plot'"
     ]
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(df,index=pd.date_range('20200101',periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.046226</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.478348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.429763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>1.073707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.115438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-22</th>\n",
       "      <td>0.187545</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-23</th>\n",
       "      <td>-0.855818</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-24</th>\n",
       "      <td>1.241236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-25</th>\n",
       "      <td>1.075209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>-0.029026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0\n",
       "2020-01-01  0.046226\n",
       "2020-01-02 -0.478348\n",
       "2020-01-03 -0.429763\n",
       "2020-01-04  1.073707\n",
       "2020-01-05  1.115438\n",
       "...              ...\n",
       "2022-09-22  0.187545\n",
       "2022-09-23 -0.855818\n",
       "2022-09-24  1.241236\n",
       "2022-09-25  1.075209\n",
       "2022-09-26 -0.029026\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d9f5290>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>cumsum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.046226</td>\n",
       "      <td>0.046226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.478348</td>\n",
       "      <td>-0.432121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.429763</td>\n",
       "      <td>-0.861884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>1.073707</td>\n",
       "      <td>0.211823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>1.115438</td>\n",
       "      <td>1.327261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-22</th>\n",
       "      <td>0.187545</td>\n",
       "      <td>-28.149739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-23</th>\n",
       "      <td>-0.855818</td>\n",
       "      <td>-29.005557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-24</th>\n",
       "      <td>1.241236</td>\n",
       "      <td>-27.764321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-25</th>\n",
       "      <td>1.075209</td>\n",
       "      <td>-26.689112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>-0.029026</td>\n",
       "      <td>-26.718138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0     cumsum\n",
       "2020-01-01  0.046226   0.046226\n",
       "2020-01-02 -0.478348  -0.432121\n",
       "2020-01-03 -0.429763  -0.861884\n",
       "2020-01-04  1.073707   0.211823\n",
       "2020-01-05  1.115438   1.327261\n",
       "...              ...        ...\n",
       "2022-09-22  0.187545 -28.149739\n",
       "2022-09-23 -0.855818 -29.005557\n",
       "2022-09-24  1.241236 -27.764321\n",
       "2022-09-25  1.075209 -26.689112\n",
       "2022-09-26 -0.029026 -26.718138\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cumsum'] = df.cumsum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e550e90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cumsum'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.4.3 多样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x120ec48d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(np.random.randn(1000)).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(1000,4), columns = ['a','b','c','d'],\n",
    "                  index = pd.date_range('20200101',periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.540521</td>\n",
       "      <td>0.264493</td>\n",
       "      <td>-1.247045</td>\n",
       "      <td>0.976396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.025493</td>\n",
       "      <td>0.094312</td>\n",
       "      <td>1.078191</td>\n",
       "      <td>-1.181591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.340604</td>\n",
       "      <td>-0.280556</td>\n",
       "      <td>0.820088</td>\n",
       "      <td>0.352325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>0.348915</td>\n",
       "      <td>-0.035368</td>\n",
       "      <td>-0.536349</td>\n",
       "      <td>0.651380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-2.160581</td>\n",
       "      <td>-1.349989</td>\n",
       "      <td>-2.523320</td>\n",
       "      <td>-1.191397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-22</th>\n",
       "      <td>-0.383892</td>\n",
       "      <td>-0.822608</td>\n",
       "      <td>1.621530</td>\n",
       "      <td>-0.523022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-23</th>\n",
       "      <td>-0.515754</td>\n",
       "      <td>-0.900910</td>\n",
       "      <td>0.039698</td>\n",
       "      <td>0.548958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-24</th>\n",
       "      <td>-2.001848</td>\n",
       "      <td>1.415893</td>\n",
       "      <td>0.641034</td>\n",
       "      <td>0.788675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-25</th>\n",
       "      <td>1.556356</td>\n",
       "      <td>0.766884</td>\n",
       "      <td>0.394139</td>\n",
       "      <td>0.353637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>0.013683</td>\n",
       "      <td>-0.409037</td>\n",
       "      <td>-0.147776</td>\n",
       "      <td>0.457222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         d\n",
       "2020-01-01 -0.540521  0.264493 -1.247045  0.976396\n",
       "2020-01-02 -0.025493  0.094312  1.078191 -1.181591\n",
       "2020-01-03  1.340604 -0.280556  0.820088  0.352325\n",
       "2020-01-04  0.348915 -0.035368 -0.536349  0.651380\n",
       "2020-01-05 -2.160581 -1.349989 -2.523320 -1.191397\n",
       "...              ...       ...       ...       ...\n",
       "2022-09-22 -0.383892 -0.822608  1.621530 -0.523022\n",
       "2022-09-23 -0.515754 -0.900910  0.039698  0.548958\n",
       "2022-09-24 -2.001848  1.415893  0.641034  0.788675\n",
       "2022-09-25  1.556356  0.766884  0.394139  0.353637\n",
       "2022-09-26  0.013683 -0.409037 -0.147776  0.457222\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f657910>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>-0.540521</td>\n",
       "      <td>0.264493</td>\n",
       "      <td>-1.247045</td>\n",
       "      <td>0.976396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>-0.566014</td>\n",
       "      <td>0.358805</td>\n",
       "      <td>-0.168854</td>\n",
       "      <td>-0.205195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0.774589</td>\n",
       "      <td>0.078249</td>\n",
       "      <td>0.651234</td>\n",
       "      <td>0.147130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>1.123504</td>\n",
       "      <td>0.042881</td>\n",
       "      <td>0.114886</td>\n",
       "      <td>0.798510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>-1.037077</td>\n",
       "      <td>-1.307108</td>\n",
       "      <td>-2.408435</td>\n",
       "      <td>-0.392887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-22</th>\n",
       "      <td>12.708852</td>\n",
       "      <td>2.524530</td>\n",
       "      <td>-1.570719</td>\n",
       "      <td>69.214720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-23</th>\n",
       "      <td>12.193098</td>\n",
       "      <td>1.623619</td>\n",
       "      <td>-1.531021</td>\n",
       "      <td>69.763678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-24</th>\n",
       "      <td>10.191250</td>\n",
       "      <td>3.039512</td>\n",
       "      <td>-0.889987</td>\n",
       "      <td>70.552353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-25</th>\n",
       "      <td>11.747606</td>\n",
       "      <td>3.806396</td>\n",
       "      <td>-0.495848</td>\n",
       "      <td>70.905990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>11.761289</td>\n",
       "      <td>3.397359</td>\n",
       "      <td>-0.643624</td>\n",
       "      <td>71.363212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a         b         c          d\n",
       "2020-01-01  -0.540521  0.264493 -1.247045   0.976396\n",
       "2020-01-02  -0.566014  0.358805 -0.168854  -0.205195\n",
       "2020-01-03   0.774589  0.078249  0.651234   0.147130\n",
       "2020-01-04   1.123504  0.042881  0.114886   0.798510\n",
       "2020-01-05  -1.037077 -1.307108 -2.408435  -0.392887\n",
       "...               ...       ...       ...        ...\n",
       "2022-09-22  12.708852  2.524530 -1.570719  69.214720\n",
       "2022-09-23  12.193098  1.623619 -1.531021  69.763678\n",
       "2022-09-24  10.191250  3.039512 -0.889987  70.552353\n",
       "2022-09-25  11.747606  3.806396 -0.495848  70.905990\n",
       "2022-09-26  11.761289  3.397359 -0.643624  71.363212\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.cumsum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1211c1d90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HZJGiKLcB8cC1Z3qT1FdfpexIw6YaduvZg7ZPPlnjmF27drFw4UL27NmD2Wxm4MCBDBo0qEHlkEgkdaNw3ToAAm66sYklaRk0iJIXQuwFBrvoOj2fn2bGv//+y1VXXYWnpycAV1xxRRNLJJGcu+QuXozHgAHoW1ne+MaiReWuqW3F3ZjIVMMSSdOTv2IFxthYQh55WH4n64hMa1AHRo8ezdKlSykpKaGgoIBly5Y1tUgSyTmHsFhIffEl9O3b43f11U0tTouhRa3km4qBAwcybdo0+vfvT4cOHRg1alRTiySRnHMY4+Kw5OQQ8tijcsO1HkglX0eeeuopnnrqqaYWQyJp1ZgzMig9fBjvMWOq9JUXsTa0b12Fuhsbaa6RSCTNhoRZ95Bw192Y0tJImHUPhf/+a+8zJScDoG/XrqnEa5HIlbxEImkWmNLSKT14EID4m2/BGBtL4b//0vPgAbU/KRl0ulafjKyhkSt5iUTSLCg9dMh+bIy1pRE2mxFCqG1xcejbtj3j1LvnGi1CyZf/JzdnWoKMEklzptzm7jl0qFO7MTYWU3IyBatWSVPNadDszTXu7u5kZWURGBjYbP1ihRBkZWXh7u7e1KJIJC2OgrXrKNq0CRCg1xN8372kFRURePvtJM2dS9GmzWofEDDz/5pU1pZIs1fyERERJCYm0tzTELu7uxMREdHUYkgkLQpzdjaJ990HVisA2uAgPM8/n46LfwEgLTSU3CVLMJ48icbTE5+LWkUQ/Vml2St5vV5PR5lpTiJp0RhPnULj6Vll07Rk3z6wWnHv1YvSw4fReHg69ftNmULWZ58BIIzGsyZva6JF2OQlEknLJnbqtcSMGk3G+x8AqonTUlhE8Y6dALSZNg0AS1aW03XB996Dxsfn7Arbymj2K3mJRNKysRYXYy0oACDz44+xlpViTk0j/6+/7GO8RgwHQKm0r6UYDHTdvImiTZvRtmlz9oRuRUglL5FIGpX8f1YC4Na1C2Uxx8n+6usqYwyRkYS99ppL7xmNwYDPuAsbXc7WijTXSCSSRqVk924Awl55xWV/u7fVUpptrpqC19AhZ02ucwWp5CUSSaNiyc3BrWsXPPr2xb1v3yr9PuPHN4FU5w7SXCORSBoFc04OGe+9R8HqNXiefz4AUQt+onj7dgo3bMR77BgK1qxF4+bWxJK2bqSSl0gkjUL8zJspO3YMAK0tNbCi1eI1fDhew9WNVq9hw5pMvnMFaa6RSCSNQrmCB9AFBjahJOc2UslLJJJGQV8h77suJKQJJTm3keYaiURSb4TRiNVoAmHFWlCALiwMYTTa7euWvDysxcX28VpZyanJkEpeIpHUm5Tnnidv6VJ0YWGYU1IIvON2sr74kq7/bSb1pZcoWPE3AIF33ol7zx74TJjQxBKfu0glL5FI6k3e0qUAmFNSAMj64ksAinfutCt4AEOH9vhOmnT2BZTYkTZ5iURSLzLef7/avpJ9+5zO3c87r7HFkdSCVPISiaTOmDMzyfz4k2r7i7duA8D3isn0OLAf927dzpZokmqQSl4ikdQJYTSS8syz9nP3Xr2c+hWDwV7CL+Shh1D0+rMqn8Q1DWaTVxRFC+wEkoQQlyuK0hFYCAQAu4GbhBAyIbRE0kIp2LCBwnXrAIj84nO8hg9HlJWheHhgSkykLCaGxHvvA6TLZHOiIVfyc4AjFc7fAN4RQnQFcoDbGvBeEonkLGItKSHjvffs596jRqHodGi8vFA0Ggzt2+M9dixuvXoSNPv+Zluq81ykQVbyiqJEAJcBrwAPKur/8Dhghm3IfOB5oHpjnkQiaXaYMzKwFBaSs2ABxuMnAOi2bavLsYpWS6clS86meJI60FDmmneBR4HyEi6BQK4Qwmw7TwTCG+heEonkLHF8wsWI0lK8Ro60t2n9/JpQIkl9OWNzjaIolwPpQohdFZtdDBXVXH+noig7FUXZ2dyLdUskrQlrcTEWW8Wm6hClpQAUbd4MQIcff2h0uSQNS0PY5EcCVyiKEoe60ToOdWXfRlGU8ieFCCDZ1cVCiM+FEIOFEIODKxX5lUgktSOEIGfRIiy5uXW+xpScTPTAQRwbOgxrHQtkK+7ueA4adLpiSpqIM1byQognhBARQogoYDqwVghxA7AOmGobNhP4/UzvJZFIqmJKSiL12edIuOfeKn15v/+OOSenSnvm55+rB1YrxhMnXM5b+O8mp3NF5n1vkTRmWoPHgIWKorwM7AG+asR7SSTnLJYcdQVfXmavHFNKCsmPPY7n4MF0+OF7p76y48cd49LScO/Z035euHEj5oxMCjf9izYoiKgfvif/n5V4jRjRiO9C0lg0qJIXQqwH1tuOTwKyYKNE0gjkLllK6cEDhD7xhJOZxpyTg86W8dFkyytTcuBAlevNGRl4DBpEya5dmNPSnfoS7rwLAI9+/XDr2gVDVBRBd93ZWG9F0sjIiFeJpIWR9MijpDz5JDk/LSDpwQcxJSXa+3J//hkAYbGQ/MijVa41paeT8fHHmE7F496jBxpfXwrWrsGcmUn622/bfxhAzUOjD23b+G9I0qjILJQSSQtCCEH+smX284JVqylYtRoAfWQkuYt/xX/6dIr++w9TUpJ6TVkZxsRENB4eHB89xn6toUMHAm68gcyPP+HExEuwFhUhrFan++mCZEWnlo5cyUskLQhLZiYAbt27O3doNPhffz2mxESODRtO0oMPOXWfGD+B9Hnz7Of6iAj8Z1yP7+WTAbAWFQGQ/dXXTtcF3HJLQ78FyVlGKnmJpAVhTFRNMyEPPoA+MtLervX1Rd8urMZrzelqHIrXBRcQ9csiFJ0OQ1SHKuM8BjvcJGVt1paPNNdIJC0IU7IabqJv146onxdS9N8WjLGxeI+7EFGh3B6ALjgYn4svJufHHwE1oMlj8CAiv/jcnltG0WjouuU/Mj/5hJzvVA+cyI8/pvTIUcyZMjixNSCVvETSgjAlqUpeFxaG1tsbv8svs/eVxcY6jfWZOJHg2fejbdOGzI8+AqsVj959qiQP0/n7E/rII+R89z2+l05C6+uL11DpGNdakEpeImlBlMXE2BV8ZfRhDnNN182b0AYEoCgKQXfdqSp5wL1PH5fzKno9XTZsQBcgC263NqRNXiJphhTv3EnKc88jhHPKp7Lo6GqrLWnc3QHVTKMLDHSYZAwGPIcOBcCjf79q76kPDZGFPlohciUvkTRDTv3fTLBa8bv8MjzPPx8AY2ISZceO4T12bLXXddu5A1f5ASM//4yyI0cwVNislZwbyJW8RNLMMGdng81f/dRN/weo/vG5vy4GsCt9V2i9vdF6e1Vp17i54dG/fyNIK2nuyJW8RNLMyPnhR/uxYjAAcOqGGynZvRuNry/eoy5oKtEkLRC5kpdImhmmtFR0ISH4z7gexcMDIYQ9+ZjGq+oqXSKpCankJZJmhjkzE11QEPr27bHm5VF68KC9z+IibbBEUhNSyUskzQxzWjra4CC77T3u2uvsfQG33NxEUklaKtImL5E0I0xJSZQdPYrvJRNx79YNxWBA2Co3dVq+HLdOHZtYQklLQ67kJZLTQFitVXzYGwJjopo50qN/fxS9HrcePex9ho5RDX4/SetHKnmJpA4U/fcfZlsGSICjvXqT9tLLDX4fS5Z6j/LEYBHvv4f7eefRcemSKukIJJK6IJW85JxDCKH6otcRS2ER8bfeRswFozAlJ2MtKQEg56efGlw2c2YWAFqbkte3bUvHXxY5leeTSOqDVPKSc47MDz4kZsRIe9re2ig9fMh+XLxrNylPPdWg8giTCWN8PKaUFNJefRUAbZs2DXoPybmL3HiVnBMIo5HSo0fx6NuXnIULAShcu5aA//u/Wq8trVAjNfuH7yndt79BZYu9bhplR444tSkauf6SNAzykyQ5J0h59jnirptG2clYRGkpAKXR0Zjr4HdecuAg+vBwQh59tIqCtxYV2TdLT5fKCr7dm/87o/kkkopIJS85JyjcvAmArC+/xGorrpH36xJiho+g5OChaq8r3rOHgr//xr1XLwJvvQW3rl0Ah838xOTJnBg/nuIdO05LLmGxODdoNPheeulpzSWRuEIqeUmrp/DfTVgyVK+VvCVLAHDv3dven/LsM9W6Q2a8rdZF9ZtyJQARH3xAwK230unPZaDXY05OAcB46tRpyWbJz3c613h7o2i1pzWXROIKqeQlrZ6EO+6o0hZ455325F9lh49gSkioMsZqNFK8Zw+Bd9yOz0UXAWCIiiL00UfQ+fuj9fS0jxUW62nJZsnJBcCjn5rnPfj++09rHomkOqSSl7RqrBXqnoZ/8D7agAAAPAb0p+vGDUR8+gkA5owMjImJlB47Zh9vSkoCiwVD584u5zbYTDcAlpy6u2RWpGT/PgBCn3yC7nt2E3DTjac1j0RSHdK7RtKqMWepfudhr72G74QJ+Iwfj7WoyF4+T9+2rTouM4tTN6gKtseRw4jiYtL/9yZAtYU2It59l4LVa0ifNw+TzWxTX/IW/4q+fXvczztPetRIGoUz/lQpihKpKMo6RVGOKIpySFGUObb2AEVRVimKEmN7lcUjJWcdS24eAFo/PwAURXGqj1oeWWrOckSzxk2bTvSgwRSuWweAWzXl9nRBQfhPn4Z7t26URUeflnxlx4/jNXKEVPCSRqMhPllm4CEhRE9gGHCvoii9gMeBNUKIrsAa27mkhVG4abN9NdwSyXjvPQC0bfxc9msDAlDc3TGdOoXG1xeA0v0ON0nP4cPQ+vjUeA9D586ntfEqzGYsubnoAoPqfa1EUlfOWMkLIVKEELttxwXAESAcuBKYbxs2H5hypveSnF1MKSkk3H47yU8+2dSinDZFm1TXSa1NgVdG0Wpx792b4l27q3i1tH3uWTp8802t99AFBmLJzcUYH18v2cpzw+sCA+p1nURSHxr0GVFRlChgALANCBVCpID6QwCENOS9JI1PwarVAJhT05pYktOjaNt2+3G5X7srfMaNo/TgQSw5OaqXi1ZLxMcf4X/99XW6j8ZXXemfuHhinWUzpaWROHuOKltA9bJJJGdKgyl5RVG8gV+BuUKI/NrGV7juTkVRdiqKsjMjI6OhxJGcIcV79tjzqCh6fRNLUz+McXFkffUV8TNnAhDy8EPo/KvfEvIePcp+7Hf11fTYvw+fcePqfD9rBV93YTLV6ZrMTz+lZM8eQK7kJY1Lgyh5RVH0qAr+RyHEEltzmqIoYbb+MCDd1bVCiM+FEIOFEIODg4MbQhxJA5A4e7b92JzdxDb5uM2QVn1UajnCbCb93Xc5cckk0t98y95e24q8oouke/du9Q5G8rvqKvtxafSxGkY6qPh0JFfyksakIbxrFOAr4IgQYl6Frj+AmbbjmcDvZ3ovSeNjyc3FlJSEJcvh921OS0eYzU0jkKkUvr0UPr2gxmFCCHIWLCTr08+c2gNuuaXW4tcVPVsMXbrWW0RDZCSdlv0BgDH2ZI1jTampFKxf7+SNI1fyksakIfzkRwI3AQcURdlra3sSeB1YpCjKbUA8cG0D3EvSyMRee509+jP8g/ex5OSQ+uxzGOPjcevU6ewLlLRLfRU1R5Tm//kXaa+8UqW9vil7td41/yBUhz4iAoDkRx7FY8BADBHhLscl3j/bKaslYPfqkUgagzNW8kKITUB1JWsuOtP5JWeXcgWv9fPDa8gQNbeKTkfe0qWEPPTQ2RcotYJCtFph74/g3wE6jnYaVnpQHRd0/31YsnPwmTCelOeew2fchXW6TcffljpFx9YXjYeH/fjE+PG0//orvEaMcBpjzshwUvBdt/yHKSFBVnySNCoy4lUCgDknB1FWBoBb1y50/PVXFIMBrZ8fHuedR/HOXU0jWG4Ft8TFt8Dh39TjvtNg8vugdwfUiFV9ZCTB995rH97ln3/qfBv3CrVUT5fQJ58g7dXXACiLjXVS8iWHDpE0Z679XN+hPTp//xo3hCWShkAqeQlCCGKvucaeUdFn4iX25F0Abl27UrB69dkXzGyErR85zssVPMD+nyG0N4xU3RCN8fHoQpvWS1cX5AhqSnvpZRSdHv9p1wGQMe8dTLZKVFG//II+vF2TyCg595Cx1BIs2dl2BQ8QcPNMp359WFss2dlYbXD4PqwAACAASURBVCv9xkZYLFj3/AqxG6t2RgyBIXepx0m74fDvlO3eSOmBA3gPPPPV+Jng1qMHmgopE1Kfe85+bLYlMGv3xut4nNcHXYDcbJWcHaSSl2BKSQXAe8wYuqxZ7ZTbBUAXqibxOt2c6fUl67XHiL7+acx/Plu18/J5cOn/wM0XDv9G2Ze3Uvi/6QD4BcSeFfmqw61TJ7rt2E7Abbfa28yZmVhLSiiLOU7g7bfhd+WVTSihpKVTarIQk1ZQr2ukkpdQuHYNAEH3348+vKpXSHmBjdgrHAqqLDaWE5dMInfJ0gaXJ+OHvwAo2n9CbZi9x9HZ9jz11SuY7GNenFweQvpePxSNQJ+wDApdhmOcNRRFIfSRR4ha9DMAx8ddRPpbb4PJhMegQU0qm6Tl8/qKo0x4ZyMpeSV1vkYq+XMcc3Y2uUuWori54dbZtYukW7eqvuPFW7eqkaWff96g8lSs0GQptQUl+UXCDb/C7WvsfdbwoWQccCQOE1ZFdbNc/UKDynO6ePTtS+jTT6OPiCDnxx8BcOvSpZarJJKqlJktPLhoL7GZRRxKVrOqPvzLvjpfL5X8OYzVaOTk5ZMxp6biP2OGkxtgRRRFIWi2WrHIWlJC2cmTpL7wIqCmECje5drzRlgsWI3GugmTcwqOraR07157k7lUC5PeBK0euo6HiMH2PmPARVhNGkIHqB/6gMHeMPQu2PsDPO8HxtN3h2woAm68gQ7fzbef68PCmlAaSUuioNREiVGt/7spJpMlu5N45a8jhLdRv6OpeaUcScnnyaUHMNVSlUx615zDlOzZiyU7G21gIG2mXlPj2HI7fcIdd9qVuqFTJ4wnT3LqhhvpceQwJXv24DFgAEpJDqx6luT1WvJXrKT9N1/jNXx49ZNbLfDVxVCYStxCh9eJufsMGHqny0uK44sA8Py/l+k2Iw7t2Hsh8xhs+VAdUJACga4rOp1NyhOjaYODUHTy6yapnVKThcEvr2Zop0C+u3UIWUXqQmn1kbQKY6w88PNejqYWMLxTzWkx5Er+HKYs+igAnf74HbdqStyV4zVyJADFO3eCzaRSbncGNW/7qRk3kPvLLxC9ArH7e/JXrAQg/hbHRiTGYkROPPl//+NIlXDqPyhMxVTi+Di6tfPBnJ3jUpa8P/8i7bXXAdBfcC3ayS+CTyiE9XMMSj+sPh38ejukH63DX6NxUBSFzqtX03nF300mg6RlseVkFmVmKxuPZVBqspCYU9X+nlFYRnKu2v7QoppNN3JpcY6S//ff9sAdbR3c+dw6d8Zz2DCKt261t2m9venw/Xecuun/7DljUp99Dvf7h6ItdCT5MviYsJaUYC0sRLfsJgq37CbpX3X14TmgN+17bkTRunFqjSpHYK8CSj16YM7MxBWZtrqsgHNBD3dfuOAB2PQO/FyhVmpgFwhpupo11aU4kEgqk11k5JZvdtjPP15/gvfXxDiNuXlEFN/+F4fRrJppjLWYa+RK/hxECEHS3Afs53UNq3fv2dN+7HvFZAA8zz+/yrikrzdz4q9QANz8jRgL9MRNn07MqNGIU1swFTrWFsV7DhG/xpuSoiBMhaocImQgug49sWS6zn5pTklFHxlJ+/nzq3YOvq1qW8p+KM2r03uUSJqSBdudC89UVvCHX5zIJX3a1mtOqeTPMQrWrSPnp58cDfXIm1IxSCr8f/+zH0d+8QUhDz1Ax4mq+6KpyKHEfdqpAVRlthS8xgId2THOScCK09yI+83hVUPHC9AFh2DOyMCcne001lJYhLWoCP9p1+E1dEhVIX1drJqj/4L3B9btTUokZwGrVbBkdyLRqc4+7+Wukb/dO9Kp/c2pfVnz0Bg8DToGtK9f0j2p5M8RhBBY8vJInHUPaS+9bG9X3N3rPIcuJAR0OkIefdSp3XvUBQQO0OPub8YvyuHV4jViCH4TnFMEp+70c1rJVxwPoPH0JOjuu/AcNBCEIGbESITFYu8vO6am6C3P+lgFjQZC+1RtL3Zt+pFIKpNXYsJqFbUPPA2EEKw5ksa+xFweXLSPWT86e6ZlFJTRLdSb/pFteHdafwD6Rvhx1YBwOgerzg9uOi16rbo4+/TGgQzqUHP+I2mTP0fI/uZb0iusvsvRVMhRUxuKotDz4AHXnbZskWFDcmk7OI/8fl/id9UUlKJUgtcMJeOAmk63ON0NgLbPPEXZtn/Q5h+GOIei77jkV7R+fngOcazSzZlZ6G15afL/Wo7i5obXBTXkl79jHQgLrHkRYv+FtGpklkgqsT8xlys+3MyYbsHMv9XFk2IFdsfnsDkmk7vGdMagq9t6eWNMJrfN30mHQE8ATmYUYbZY0WnV6zMLjQT7qN+RKQPCuaJfOxSlqkl1+exRxGYWcXHvtlzSJwzlnurvKZX8OUL+n3+6bA+49VaX7fUmYTt0uhAluDtK9knaXDtVbfeLQPHwqTK8zfTrUW64kdLDh8m8+hqC7pmFtagIQ1QUABp3d7zHjKFwwwbMaamUHjxA9rfzMWdm4jV8eJXUC07obD9cl7wGuQnwbh9AUVMVa+TDq6R6rvhwMwAbjmUQn1VMe5sydsWji/dzPL2QgjIzT17as9pxFSkPZjqV5VjYpOaXEuHvSWpeKbtO5TD9/Eh7n0bj2pzaNdSHrqFVv1eukJ/4cwW94/e8/ddf0XXLf/Q4dJDAO++o3zxZJ+Dby6G4gq28NB/SD0HkUJj0Btzwi9Ml3kGqrT6oj6MWanmJPfdevehx8ADBs2cT+sQTTtcFz1FLEJqSU0i89z6Kd+zAGBuLoUOHusvbJhImvgYIKM117rOYYOc3kJ8MWz9Vq08dWeZ6nuS9cKzuqYslLZ/vt8bV2B/sra64Nx+vuynwcHLV8tcJ2aod/okl+wHqbXOvDankzwEseXmUHjhoP/caMQKdvz+KVlv/ghV/PwFx/8LBX9VVMkDyHjWlQGRVTxsAN18LPacn06aT6yjU6oKEDLZKVElz5zq169vVM3LUz2a///ZyKCtUj2NWw0tB8Odc+HI8/P2YanL6+UZ7HIATn4+Bn65TnwYkrZZOQV5M6e3PtD4+zP/vFAnZ1UdOF5SpRdsPJecTm1lUp/mPpRUQGeAcWZ6Qo94jPrsYPw89Vw2oZr/pNJFK/hyg7MTJhlFOQkDSTvV4+cOqGWTty2qkKUBIL9fXTVNzt+g8VBk8hw+r0+007u54jx1bpd3QuZ45YIK7q6/ph+C1cDi1BX6sEOGbn+Q8PttWp7U0T/275TvSMJPRdIFVksbjREYht3yznfjsYp5KvIs3jk/GaLEy5s119vQC5fx3IpPuT6/gYFI+XUK80WoUrv98K3sTcquZXcVotnIyo4jJfdvh56Hn6oGqJ9iji/eTVVhGSl4pVw8Mr7N9v660KCUvhCB/5cq650M5hxFmM8lPP03Z8eMYY9UUvB1+/IEuGzfUfrHZ6Ho1W5oLxZV81ze+qa7s9Z7gU80Ku+flMOxeFAW6LfmUyE8/rfP7iPz0E8LfmUe7t95C8fBAFxzs2nWyJgK7OJcL/OYS1+MufUt9TdimmnJebw9Lbod5FfLUpx+u370lLYIHFu7hSPRRzFZBcJnDV90qYPmBFKexL/xxmDJbINKNQ9vTro07qfmlTPloc433WH4gBbNV0CPMlz3PTGDedf3tfd9sjqPYaCHMr+7ebnWlRSn5kr17SZo9h/Q3qnqJSJwxnjpF3uJfOXn5ZHKXLAG9Ho9+/dCH1FI9yVgMLwfDpnlV+zbalKBfJHgGwqiH1fPDv0PUqJp97ie+ArP3ou01Bo2bW73ei++kSfhdfhk99uym678bUfT6el2PRgs3/ebcNvRu5xTG455xBFL9Nks1QYFqlqpISt2z/0laCELwR9blbHW/nx6KQ8F/OFXNvvrQL/vILVYXllarICa9gG6h3sy+qCs3j+yIxeJYEEU9/hePuMgQmVNk5MN1x+kY5MXFvULtG6qL7hqOVqPw4brjAIT6nmtK3lQKWz6Gry8BIbDkqLlMiv77r4kFa54Ik4nkxx4n89NPMVeIFi3ZtQtDZGTdEmQl2kKqd37r3G4ucyT/uuEXePg49J/h6K947ApFgYCOtd+/sdBo1UIj5XQeBwGd4NkcmHsARj/s7Hnz1QTX8/z3PkTLPDStidIChxPBDK0jnfXlHbWE2NwZd53KgfxkjBveRidMXDc4kgcndAMgv9TsNN8vuxL5oEKkqhCCAS+t4nh6IbPGdsZd70j5MaRjAF/NdGRX7RRUg9fYadK8lfwrofDPExC/BbZ9hilVrWBkjI3FaKuXeVpYzLDkrla3Ksv9dQl5v/9OxrvvYc7IcOrT1zV/Stoh9dUryLm9vBjHsHsgpKeqECuaZ8JbQEGMBw/DHWuh3/XQaazaptFAm/aOMTMruJr6VWgfdAsMsOXDWTCtsSWVnA4luap3lCtTY3UIQe7Cu+ynNxrWO/oKUllyj1qM/VRWMSy9G/cNL3GhZg9+Ho6nSR/3qount1cds9dGOJjk8Ki5ZmDVTdWhHR1ZJDsGe1XpP1Oar5KvXOHn78cw28rUAWTMc2FOqIntX8A2W4GL7BOwfyH8cssZCtn0lOzdS2n0MSy5uWR/+y0AGi8vu5Jv+/zzAFgLCus2YbatGlNeomqbL6f8/6PTWEeboYIPsat0As0NNx/1x+iqT0FXjcmo4o9VuTln2L0w+V248ClHn8V59SZpQsxlcHwNrH5e9Y46sLhu1+WnYPx9Dm2TV9mbNFaTo//bSwl3NxLi48byAymIQjXV70ztSvwqKPbvbxvKJzdUTZuRV6LOtWBHPO56DTueGo/Whd+7h8Gxsvd2O43QJVe1kCvQfIOh4rc6n2sNmJIT7KfWklL7sRCidlfA5Tb78eBb1FzjABX/Q1sgQgjipl8PgM/EiRjj4gCwFhWR8f77KAYDvpdMJO2NNwi84/a6TZqi+upSlK66FEbYlF7538y7kk1/8nuQdrj1BBkZPOHODeqPllYHz2SCxvY18W2nvt9lc9S/R5vImueSNA5CqP80GtWNd/9C1curnNy4uk3zxTgMBclVO7pdAsdUk5yStJOZI6KY/89WrB4n0AIjtIeJj50PfdTMpl1CvOkS4s2rV51HoLeBbSez+XpzLJmFZbTxNHAstYC+4W3skayu2PDIWDwN9VTH+Smw+T3Y9kmNw5rvNzNhG2gN8HgCjHoIrBbMO37HLUSPoWNHu0Kz5OZybMhQUl98qW7zZseqq1So32NdM8SU5PiAFvyjBuq4dVU3i0RJCcJoRNumDT327MZn3LiaJxNC3VhN3A59bO6FGUcc/Tm2ItltKgUiDbpZLazdmmjXH7yD1WOt3nlDudznPu8MzIWSM+O/D+BFf0g/4nDjrUhJza6MAKIoC6WCgs+OnAAdx6gnYyukpS4r4N4Lu/C87x9YKuiL9rteq3KfGUPbM7F3Wyb0UjOwjp+3kT3xOew8lUOfcL8a5ekQ6FXjj4BLlt5Zq4KHZqrko7OjSY3fpD46u/uCTxjCYqEsT4e7Zx5+V1+FMTaW0sOHKTt5EmtBgXNmxcpUVOYfna/mGwf1P6kFK3pTUlKVtg4//Wg/rpcXyrG/Ya3th3LsE+ARACfWOvozj4GHP3jWnnu+VeNnW71LJd90bPlIfd3xlet+V0q+MF2NbAbY/gUli++2d5mBtyOD2D72AdWhoN0AeNC2wFn/OsSs4lLjP8RYw0lUKqT5/e8D9XXZHHjNsX/Tq51jg/+qj1UnkbHdg2t8S4kFiWSVuE6tXS1ZJ+s0rNkpeSEEU5dN5RptJrRXS8YJ77ZkHfXGUqbFK1LB74orASjeuQtTckpN06mUOafzJEt1V8JYAEsqlJczFqueE5XHN1MSZ80CwGOQalLxufhitD4+dF75DyGPPEzExx/VbSKrBVZUyCwZ1BU6XwhJtgx5G96E3d9BVA1Jwc4V7Cv5+JrHSRqPcvPZji8cbb2udBzHrKxa4/ez0TCvJ2Qeh+UP4xmrVi37n2kaG3tO4I+Uzby4+23HE5y3uhon46gaCwIstozmspIXuTvwKwjsClk2D5pd30JZnn0Py89Dz4WVlHq/CDVVgahmUTlpySTGLhpbbb9LRN0CHBtdySuKcomiKNGKohxXFKXG8jzFGVn8tEB99MrXavgyqT3LVu8m+d1FZOxXfx29O4AuJBjF0xNjYgLGU3H26w8eT+WO73ay7WSWc6pQV2lm2w1QXw8sUlfz69+AP+5TPSdei4DCjKrXNCNKo6OxFqsfZN9L1OAeYVUj8wzt2xN42214jxpVt8mSdkFuPFz8CsyxeRz5hKmrHyEgxpaz5bJ6bna3Rgxe4N7GsSqUnB3KCtQykaCa0Mq5dr66b3LN1zD6EXUvpSjd+QcAHHtKq59zau51zZMc73mxOsToWNzlmQpJ7ToeAWQk74Dh9xHT8Sby8MYQ1Bn8wiEvyTmSPN/xdPfNLUPsgU1hfu74eepZG7+WoT8NZW+6o1g9gLWCsr5mWaVay5vfgz0/UgVTCZSbm1wVyqlAoyp5RVG0wEfAJKAXcL2iKNXEvoMmLZWBL/5kN6G8ecQH/4dnkb9RDaV3b++P1pyDUlaAISICU0IiRZsdPvM3v7+GVYfTmPb5Vrb8+TUsvk11CSyyKfkZv8Blb6vH456GCS+qx9HLYf2rzoEvR6tJVNUMsBQWEX+r+h/r0a8fbt1Vf12tX812v2qJWQmKBgbcAP5Rapt3CJiK1S9X1gnV9l550/VcxScMClJrHydpOL6/Gr6ZBMYix0oeoPcUVelrdep3+pblantOnGNMzin7oTj6l9O020u+5YM9qtkluzSbV7e9Som5hAsWXsAE8zG2ursxrn0Ef3l78/1tQ/jkhoE8PqmHarbLiYOUCgF1h5wD7h6f1INALwPf36ZGaK+JX0OJuYSbVtxEQoHqRCKEYMHRBfZrYnJiyCyx6StjMax6Fn6/B369w1k/lb+/a76Cy2tefDX2Sn4IcFwIcVIIYQQWAlfWcg1timDCbisT4rYQWKr6mOrahdFh3jPqgFP/ofH0pHDdOkp27ybWV7WT+Zgcj2gjdz8IBxfDJyMc/8leQaq/8wOHoct46DtdtT2vebGqEH8+oP5SN0OODR6MJUu132mDgvA8/3zaPv8coZWKedSZxJ3Qtq9qcy/H22Z7zDwGJdkQUHOh73MKn1Cp5M8mBamqQwCo2U/L3Xm9XZTB84+C0POcv7s7vsCMwp9envzj5YFR48FLpht5wDiLpcedI5oXHF3A7LWz7eervVQ34b+L41EUhUnnhdGujYdaNL44Exbe4Li4PIeTjSv7h7PrmQl0CVFTAueWOfYKfj/+OwCHsw7z+na1KP2EDmoA3sFMWzLB7Ao29wOL4I859tONJ/7iGz8fhH/tAYaNreTDgYQK54m2NjuKotypKMpORVF2lre9Mt/CjPVW7t273D5OF9UJTfex4BsB617GWujw+14TabNJ2+xwvZU4ZymW2NwHvYLUyEc/mwg+odBpjHPSKfcKaT4b4JE8pzTnjOeoCZ9x41AUBf/p009/JV+YrroHVqR81f6brRpBQKfTF7K14RMGNp9pSSOTEQ1vd3ecF6So9u8LHoD7d7m+xr8D5KoLuxJzCc8lLOfm8M48ERLEIyFBHNZbmO8dyp9+rt2uD2c58hNt8Va/U9bKLsJt+zrkAQjuoabcBjXv0dZPVf99IN+Yz6MbHuVg5kHaeqk/TOWr98RC1cSj0+h4eeTLeOu9WZ+wXp2n8mdMo7XPf++x+cwL8CfWoOfB9Q+6/juUX1Zj75nj6q/otLMghPhcCDFYCDE4LhTyPSA4H7zKHGOOtYmg6NZ7QO8BI2dD6gHCnprN4YAOrGw/mD3Bqrni+a1f0zMrjk/177iWxlUCLZ9Kyq39MAjprR5XTsaVlwRLZ1Xd1KmGTUmbGP3zaLambK19cB2pnJzN7+qrznzSogzwqrT7X77xlKmW2yO4BxIb3raVfAv2zGoxJGxzPk+zrXKDuoFbNSkA/KPUhVvKPpbv/5olOhP7DI7vze/e3fBo9wvubR3RzW+Nect+nG90RKgmaFR7eXaJc61hpxQdQ+5UczmVF4vf+5OautrmfbPm1BpWxK0guzSb0eGjCfUMJd+Yj9Fi5Hiu6gSycdpGPPWe9A7sTUxujPrZSnG23VOaS+G/b3HFr44Ee49sfYFVp1ZRE42t5BOBihEjEUC1y2OLBuZfVFWkOWPnctLH9mjWQS1w65Gziu8mTuOdgdO58Rpbm8XIvH8/JJxMjELLsyZH4WkmvOS8YVOObyXFHzkUpts2Oior+XWvwL6f4KjrKkuV2ZehbmJuS9lGqbmUrJIsTBYTg74fxNs7367THBVJevAhovv2A0AXGkrb55+vfz74ypTmqxtVle3t5Uoe4LxrIaie6X1bMz5haiBdcXbtYyVnRmVX1T8fUF+DulcdW06gzbT42WiOb1JjODRCwZqn2sZ/1jkX5Zg3dh4Toyay9Iql1U55MOsgpWZHAKbTosjNV/1Xmgd7F9hdj5ek7+CChRfw6rZX7UM7+nXknv7q0/GXB75kRewK+gb3xcegmnQifCJILEhUPXbKzchTPoVRD5Or0bBty1vEljiyARzLOUY7r0oL1Uo0tpLfAXRVFKWjoigGYDrwR3WD23m1I6eailbL9tl+G0J6qpuE2z/jJ+tj3DO2M7dO6E3UYkcos7lYw4EBL9B+zM2OCSok0BJmM5lffEHe779TlFkhaRWo7oOetlwSlaLhrFZbKHvllAvV4KZVgxtKTMXMWj2LsYvGcv/a+zFajXx76Ns6zVFO4uw55C93mK86Ll2C//QGyKFyaIn6anNXteMZABHnq4FR13x55vdpTZQvOPKlr3yjU7469qgUn2F7endJX8f34qReT6+yMs6PvYmpHR5AW9YVraez+2tbT/X/s4t/F36f8ru9vWeAc0m/e9fcy76MfSTkJ6gBcpNsQYBRI1V3xrQD8NvdcFjdgH2u6DB5ZXmUWhw/Dn2C+uBnUE1An+z7hKSCJIa0daTO7urflezSbLbZXDy57nvofz0ZQ+9gVIcIngly5Lkp56quNT/NN6qSF0KYgfuAf4AjwCIhxKHqxnvpvTgW7rwyjRvUDo0hjVWHU1l1OA2jVbH7h3opZYRp1M0Mj+6diRyjrrzNJVoGXXQdl5zfgz8tQ4kPGu2UcCvrq6/JeHseyY89Tvyjr1PqVUHBeQWrAVhh/dRIuhKbTb0gFc3+nwFIOV7pMaoyhRmw4jHy4/4FIG/nF+xMU7ccNic7ck5bhZWM4gw2JGzgtW2vubTfW4uLSXnmWQpWrnRq1wU0UFBSui3oI6qSu6WiwO2rYerXDXOf1kSYzR6bsL1p5aiO1mRGKs1TE8g9csLRNuohNQ9RdRi84EnVVh5r0NPRZGZ/WRhhbTy4ott4+7BVU1fxwogX6BPUx97Wya8TK69ZyeLJiwmw/bD4u6kOCdtTt3Pj8hu5dOml6uChd8GDR9WMpp3GOItd6Qm7s19nnhv+HP1D+hPq5XhKNgsz7bwdK/HLO10OwJG842qeqF5XAHCiIA6AAlvBb0+dJ1G+UQAMC6u5CE+j+8kLIZYLIboJIToLIV6paaxBa2DX7QfptnMnnf5cxrefXMajE9Lw6vwOhsAN3PHdTro9vYK3TVPt14w4bjN7pB1C56H6iZtHvQY+oUT4e/KSx6OMTrwLoy3Jv/HUKTLecbbZl3WdpUa6jXsGImy/qkNsQVJvREFpPhv/+NY+3pJWQ+EIi0l1edr2KXEJmwDY4e4crnxtt2sBWH1qNeN+Gcd9a+/jp6M/MXfdXN7a8ZbTY+HxfxaT+4taMzVg5kwalKN/wTZbAY/y4teS2vHvqK4s06pdrzQNJ9bCm13hhTZQkKZ+dg8uaWqpzozSPHD3c86NNOL+2q8zePKJZSIpOh1/FF9OOv6M7hrM06McOZxCPUO5uuvVVUyeYd5hdA/ozhNDnmBku5Hc1e+uyrOTW14vuNzcO/xemL0XHjsFUz4h06aMrywo5I7cPJZOnM/Ubqre6hPUh8mdJtvn6uzn8Fzzc/PDR9HxnzkHOl1ob4/Pdzx99A3uy/pp6+nm383+Pmqi2UW8Ami9vXDr0oVB7YbY84a4hfwNGrXg7QeWq+hS+h3LrcOILLVtDCbtsit5U6mO/OXLKVi7lv6RbQCFtPxSSg4e4sREddPCe4zjl9eYlKhGulXMKd7f4RqVF7OZ40f2UCTc+NZ8MUElJzGbLWw8llE1Qm3lMxCzEjOw010NhkirlMf9ocEPoaDwzi7nH5vd6buZf3g+n+3/zN4W/d5r9mO3bt0IfeZpwt99tx5/zRqo6cdKUj2KonpoVS4b2JQc/BW+v0rdXwHY/rn6FLpsTs3XNWeEUGNY9LZsp5e8AVd84OzqWw174nN4U6Ou2o3GdjxzeS96tfPFoHUsZmrbz+rg24FPJ3zKjB4zuLrr1U59O9J2VL0goCN4tIH+M8i8TI2SnVhUzOycPJQ32sOq5+zmpyeGOorWnxd0ntM0fsYStnh48I+/w+6fWuRw2e0Z0BMPnQcvjnyRz8Z/Rph3zTWPm28WStRNiIrofQ9gyh0CKOx94TK8d5xU04seWQbHV6MNbY9iUChY8Tcl+9RNz2m/reefQ2lkJqRQOM3xBBD+zjyiB6qul+Z0F9GtFT4Azy7YyOP67RwSUUSLSNytJcR8fj3/Fz+Tl67szfSCb9Ft+wjF4tjBXzThEQqO/8x4n86sLjjhNLWX3osovygG/3mc2FCFnd006DQ6zDabf1xeHABlJ0/SpYI7tlvXLnj07VvfP6OKEGrOnhNr4eKX1Ijf8j2HPlNrvlZSFd9wNd9PzGrQu6sRw7UVTmlMKgXi8K/NW6QsX42O1HtUvaa5U56JtrzYy7C7qx9biffWxKBxU7/XVmMwV/RzmES+nvg1Raa6Fd4G9cdgAiEv0wAAIABJREFU7sC5LIlxPBVtStpk92t3Rbq/6m8SYqlQH3bzu+qT/iWv4mPwYf//7cciLOgqBndZzNyYX8DrgQE8vPN1ekaOYsHRBfxw5Af7kPY+ap4cL70XI8JH1Cp/s1zJl9M7sDcDQwbyzLBn0Co6IkLzWHDnQL655Xw17/LAmeAVAiseg/QjKBHnow0IsCt4gOCjqv28cP9Be1vYa6+h8fSky9o1TrnXK2KxWhCz1A/Ze4aPCVOyKe55HZYI1X7fNf0fVhkeIfrPd1m77xtSsfB4cCArPT0w9Z/BZ4mr6BXYi6f7qyspDXBTXj7vpGVAwnb6tOnJtZsEj/6qmpGeHfYsy6aoUbbl4dWFsap7VarNGcCtRx3cGIsyXdtko5fDmhfUeqzRK9S2vEQ1cGRqNYmeWgH/ncikqKwRcr9H2Kr5/P0YfHuZWjIwZR9YzPXLP9JQJO6E866D+1z4jjfToL4aKStU04wATKk902Jl2vq6o/M6hhY3ltw+xSnD4/ltz2ds5Nh6zefv7s+gUHVROCh0ECdzHYFKJouJhUcXYrKlLs8uzeahDQ8BEGR2LgJe0fddURRnBQ+Qn0TbCtd8uPdDJwXf3b87kzpOqpfszVrJ+7n5MX/SfK7rfh3dA7qRqVnDnf9eTLtg2walZwBc+qb62JwXDz5t8btqitMcusfnAmDdvsXe1sY2Rt+uHV4jRlCya5c9DwyoART9v+9P37+vY7lvIDeEhXJleBgjLxiFf4feJFjVx6iumiRe0H/DwyG+XBbegb+8vXgoNJg3RCbZpdnc1/8+AgO78WpGJotSs3k0O5fxxSXw1QSu93NsAC2evJgpXaYQ5RfFhA4TiM2PpWjbdrI3q0W3X5muxWf972gM1djNd30LP16rBo682VktUQeqXXbF46qdduEMdUVk8FZDooVQc9aEneaTQQsgJq2AGV9s49XlR1i6J5FFOxNqv6iuXPAQaPSOZHfAjwsu5bwfBvDiVhcR1A1N9N/w4RA1KVZhuvpU1q6/6ur6ZDKMnOsI2HGVu6m5kxGt/m1HznUkDasjxaZi/iq8AX2b3QwO60//yKoeKafDZxM+Y9P0TXTy68TejL38ekyNlv3ywJe8su0VFh5dSJmljBWxK+zX+I97DmZWSJFSWxDdibV4VMi7VXGuCO8IFl+xmGDP+v09mrWSr0jFzYmVp1ay6tQqJi+dzN0Jf3BSb/s19AkjZM4cuu3YTurL6sapxtubNpZSPParKxx9+/ZO8/rfMANLXh6Fmx1eL3vSHfkoHgv0Yr+7GycNel6K/wuzFW4yOfKs5dhs+CatY7X4c74aQTu83XDwDmFyYQndSwrBJ4zSXB1HFrYj+C+HZ0ZnS4DdPujn5kd6cTrxM2di/kl9PMz2gWPGRErMJZgsLgqdLJuj5p/5yLZpXJ6CtTzfdLmddtIbqpkm67i68izOgrD+Vedr4RSWmdkUk8nCHapST8kr5YGf9/Ho4v3/3955xzdVvX/8fZIm3XtAS1tKB3tvlL1EUEERAXGA4gJ/iusrbgQn7o0gggqKigMFZMkG2UJbNoUChdJduts0ub8/TmbTFpBCC9z368WL9Obm3puc5LnnPOPznNcse82JNYxfPp5tqdVkz2g00MwWPCsUgrcCZTbGwkPn2ZnoYlg8SRaqZR2xtbEMlTUU6D1hwKtwi/lmn5VU9zNuTu1yFPuyVJK2uPBiv+8Tbfo0z3d5/mKvzIqr1hVfV1+89LIIa8o/UwCbvZi+fTqPr36c7BJZP3Fz9M1ouk+CRj1llzH/RnIlnbpHKltO8XMelwOLaeDpKNcQ4hHC8uHL+fbGb//TdV8xRr5FUAvr45nxM3ly7ZMk5yWzKW07qz3MgRlzr06ttzePFX7N320EpoICfvjzRbzTUggcfz8xfy2lsLScNQel4fPo0AGh01ESLzsizdgzg4dWOkfTAX478hsP9owmWanPjPKbSXZvQZZWW+m+j7V7TC7FtDpbcUZUd4qQbcJy5tmWYEdvswV1hscNR2N3Jy/Vgc7Nk8fXPE7n+Z15et3Tjic6sBQnLNk5FfVVonpIQ3BqJ+wxiyJVlDO4wigxGDmSns93W45bx3TMrC3cNXsrszfKRierD9jqGlLPllR6HAsGo4Gn1z3N1jNb+SOpypIOiTkt19Dydu4Kqz7DoUY5sNRmBA+vgN8eBhd328zdgoc5bXjRBCl0VVc5vhlm9YH1ds1ntnwu/6+sSv0czEtYjKnMj9HBC4j2rXk5juvDrrc+3pu5l2N5x6x/bzq9iaTcJKJ8onijh60IioBom3jawvvNjU4Ux5ob8+q6YVRv1tyxxpqBE+4VTphX2AXP4C1cMUZ+YMOBVT5XZOmbaC7o2ZslU9vyK8SaFmS5MuGH3Yz5aivj5mznu3+SETod+qgoSo/I4KhFU0IjNHhrHGf9APV83Ljv+mjeKh/Nsq7fceLWzyu9pnEt7frHtpEt+ghpBu7O+jLGjEyrXEGLwBZEFLhZn9Mogo/7fmz9e/XJ1WxI2WAN0rLmDZwoSIOZvR01ef5vl2xXF93bcd+KcgZXGC/+nkj/99fz0u+JjJuznQe+3cGelLNV7p+WV72RzynNocwkx2Jf9jmyj8yzsIeNJzlSwZWmWNoo1iQH/4IpvrBgtG3bqlekO6bDvbK+wx7vUAgxi77uWeA8a1SUuiGpPcfsY177puxtkL4fjm+S8baKDeXPg4LybBRDCI/3q6Yq9iLoEtqFJbfK1cKMPTMcMl9AKknG+cc5v9AnTDaHt+jQg6NaZv4ZmREV3JQg9yCrvELXsOrz4M/FFWPkgz2Ceaj1QzzV4SnrtsfaPUaIewhpkZ3gxnesvrvFSVJ2IN/dMUVq0VnBX4ln2H1S5ri+tEjeDPSxMZQmJVFiMBLsLsv7p3aaSfrJrjTWD+fnm3+2HqPMWIbRvKxMKlnOzKSfqQz7gEpJ4ACyuQ1D1G3k77OVwnt3a4M+Uoql5S9bRllyMkIIbj9q98XWaOgS2sXh2BP+nsB7O95j2O/D2FeWLd0G/V+VT1pya0/bSaB2nWhbTcRUaAPo65jBVFf4aftJWk9ZzrTF1Rva9YccjdTKfY4+z10vDeC29jZNvLS8UqrDUpAW6hlKSn5K9e6dRrKAbFuJrXGNt1F+N0pm9qz5Zt+LKwhR2Rew9X4OJ7Qu8NAGKelRmC5jN29HwZKnZSrf1i/h3VjZErO2yHLMPOPvV219hu/9wybKdZ6YTAolxiIi/fzx/C9Nsc+TSJ9IonyiWJuyFoDfh/5udQ2dyD9B04AqkiQeWO34d+4JGWQG2asWrBOxdiGy58XtcReX/XbFGHmAR9s9ytiWY61FAB3qdaBxQGO2GHIxdbYVOexI20GQexCn7eItmT5w0t+5h+LJ7CJco2MwnDzJze+uYn9aBhH661m6Q4umsCNfD32BpgFNeamrlDl+bPVjPNYvjjFdQ1l25gsOZB+wVp4B+GdNo/DoY5zKLbZuOzbiTtIWbOHIgMEUHbQZBH3ORhr1lmp5p//3LEmDbsSQmkrH3ADy3KFID0cGyNnIu73edcinnbd/Hklnk/hSVwr1WsoCkWeTZal105ugmayUo+XtMMhutq/Rwv2rZFbN6B9tipx1jKmL95FXUs7sjcfYe/osn689QtTkJZQbHbvh6F2cv8K/PNKNXydcx6KJ1xPgqefd29uw9DFpENPzq5/Jv7JZNpVoFdSK4vJiMosz2Z2+21b8Yia7sIxn9zWicGKCddsn3d9iUo7cL9FVT4fvO5GUW8GIXQz2Y+XqY8vwCW0r87MrQ+tiE9NaPEnOFLfPgrciZWYQSENznny862PGLz/PpvB2fL3xGBsPVwgAF+fC5+Zq8x5mN+Smj2QsCQGBF66XtP5wBoooIcTzPyqyXgD2laYxfjEOMggWA+2EVzA8Hm9zQ/06Ht5rKlMrj62X4ogh8jhjW4xl46iN/9lNY+GKMvIWvhzwJZ/1+4z29drTo0EP0ovTrTOwnWk7OZB9gBGNRyCaySXTkVB4+n4to3paPizb7OzVP/ehbxgJikJRymnQlJCUVs7yvWkMaFEPXw8pamZZfm06vYnFxxfgUd8W9W4WaBvcT0b0xlQaxtqDdr42u4CSPiyEwGbmDjQa0AhHo5PzwwJ0e5PYHS0Y+6SW9HtkLu4NUTfw/ZDvub+lYxcYf5NJBnQ0WlkkEtxYCqxZmnFXFFkDiOgEj2yEJoOcn6sD5JUYKLBLe/xqwzGmL5NFb6dzbZ9XWbmJEoNMN1v8f93pESdXQLHB3rSP9KdNhDR8Go2gaX1vtBpRrbumoKzA6uqzlLonZiZy919389Lmlxz2/XJdEj/uOMlXe+VS/abom+gVPRgfc6PzX729KDOV88thR73yi8LVR2qoP5MEk+KtyqBJeecQqTuXS+4Csm9mJcxi65mtDt2Mzoepi/dx1+wKipIHl4LRvLJqMczRqMf0qVxQsBpyS3LZcnIfQlNKbHDNZNRUh858fY+3l2nSlrhhqGcoHet1rPqF/g1tPWRBtiH9tKOsYbEEzwGtRouv68XfrK5IIx/kHkTP8J4A1rvcnMQ5pBWmMXbZWEDmws4Z8zup373K2+N8KHITdGvsyuKnovBvNo2Jg+WXK8THFW2A/EIEeaxG41IIJjl4fZvYlBnbhbTjjsZ3APDujndZcHCB9bmOhSFM09zKD9qHaR3ui7+HjoQq/MKBEx7Fq7HMwvAMKYWARjRatAjXJk1AqyV73jxMeXlsai5ACKeS5UkdJrF8+HJr8KdICEfZUwsNZE4vsf3O70OtA6w5kM5XG45yIkums97RMRwvVxd++9eW5/3D9hNWF8qR9AIyC8r4cGRbWjbwZc7YTqx9urf1xmyPRiMI9nIlvRp3jX2lcaf6nQBYekwGy1LyHcXI8s03oY83SOmK4XHDEULgly1nxaXmbKk/k2qww1h+KjRoL/3U7v6sFl1YauzMxOyR7DmZi8mkcLbYgNGk8O7ygxxOM08mzmXkMw5V/7yZUqPts0svOrdI34q9Z5j8S7z1Rmx74kWZ8vv7I7Ztfg1hol0V6R3fndc1gQy+D/9iM2MW38/8U/+H0Jbi71aNtk0NYdGZ6RMhXaQ6jY7Fty7m55t/Prc6bMXnLb75JheWA38+XJFG3h6LeNA3+77hyXU2n2XrYJlp0LfTHSwZKcW9DmYf5OG/H6KcIjKULQRGLyCl8AAuAfIYMWW7mLDYyC1GhaizqTQLdQxkVSxt9nP1Y3HP+bR6YjZNXv8Z7WufIoRAp9WwYPtJvtmcDIA+JgbPnj2IXrIYv9tH4PHghzQenopnlDuk7sHth05Ez/sS7379UMz5+ilB8ktgL15kIcwrjBkDZnBdUTHJOp2cyVfEL0Lq8XSdeKEfaa1gMimMm7ud15bst8ZMRneO5PpYxxnZF2uTeODbnZQbTWw6ImegMcEypc1FqyEqyLPKc9TzcSUt39nIF5cXoygKqYXSlfZAqwcIdJPn3ZkmU289LKX1wJmzJXy/VRpzjauMAcT6mWehOrmqW2nuKJRbmssXe6ou5vkrIZX75lZSIl8RkxGykjAENGLK5insyzzKfd/vY4JhEgeUSIZ+tokZ65No8+oKlu89w6drjjDgg/XyhmiRjY4b6CDXgc5TTgbOUzp76j+2/P/TBY4KrfEZ8WQUOcZHnlkYz4LtJxnzlW0G/+jcDVJn/bCd4J5fpBQc02jgxXTpzqhKK97Mt/8k87O57iE5q5CdxzM5UWi7WTkVGV0Cmgc2J+HeBGL8bOndDX0a/vfZ95iFDmm5NcUVb+QjfWwZMPEZMmAzd9Bcq8wvgI/eB51Gx+zE2ZwtlTPsv5L/osx1N/sMX5FQLn+wDyw30TtB4b7Z6/hizXs0PGG3pEIuxxLuTeDNHm8yoe0ENozaQNEtdznsk/nlTFqZu9K88sdeFJMJY2YmuvqhuMbYgp/a55PgBpsuDe81RhduC4Jmme8v9l8gB/JO06TMwBG9DoNHFf5Yr2BHYac6zOIEW6zixd9ldXKIjxt6F1vgzdIrc9X+NG7+dBOvL5XjExFwfiX7IT5upNu5axRFYfHRxXT7vhu9f+rN8mTZsHxi24kEuksjn1EsDZe9T37HcVvwXOt6Bk+tH35ufmQXlnHfGef02893f47RZDebLSuCVa9CyVkemb+L1QfS6fXOGl790yZ4pigKmcWZtsBv7nEwlrLXw4dfDv/CxJVPOJ3H4tKaMH+XddvWY9nSYE5KlDGYHubEBVcfePoQ1G91Xl2uyoxlDimlWSU2N6DRZGTM0jH0/bmvdSUN0KSenE3vPG5TV808VKGBzp0/w6QEjIp0geWXa6Q7w0x+iYGtR+W55u+fT6tvWnEiJ5eXF+3lmYXxjJ2zjXUHM/CI/sjhsBWTFeokFW8G9Vs5z/BrgCvDAlRDiEcIa+5Yw8vdbHnAFr1mC0IIjIqx4ksBKNWc5sHtT1f63ImxYzGkOy9Lb4q+iUfamJeaBsfipIwPPuDpP+26zOyOx3j2LB4d2jsexDNQNs628835DJY+8uD7RvDFddO4NfZW64zSiYyDNC4rwyCEVevmSuarDUdpVGEWHuzlyktDmjHz7g4kvzWEHnHBPDVABt33p9q69/i6n5/vNsRb7+CTX3x0Mc9teA6jYrQWsDzc5mG0Gi16rR4/V9vN80xhGiZzbCWrwKZRpHE/SWG+XG1tO5ZNEbYbTguN7f1YbiAA/PMZbHwfdn5j3XQ8q4g5m5Ktf39/4Hv6/NSHrxLMWv5ml8opN7lCyC7Jom2EH43rVT7j7ds0BH8PHd/9Y+5v7Bchb/h+kXLlN+xz1h0v5nCBu4zbmKr3sVecuVubTQObUmxSCjvTdnKq4BS5RWVsS3ZuqtJe2LmGhBYaD6TEYKTH26t5868DvLv8oPXpxfGnaTVlBaNmr6TVN62svVA/Wr/Rus/agxlMX78Iravj77R9vQq/t7rIA3/DzR/ZVtuXKJ35ijfyIH30FvleoNLlkn2gqJGvo3uj3KXqu2fmZ8558IrBQMG6dSjltuBgvecdU9ga58jVwdQ3Zd69sV1nKsUnTKZ/Au5R9Wm6cwtBRR9x3Q9jmdroVkffXsYhmw5J5mGamXPrVx1fVeX1XymcyC6ie2wQb9wqM4ge6hWN3kVDiI8bA1vYKgCbh/k4vfZ8umP9dvg3/sgbQ25pjnVmuD5lPYBD04YJbSZYH49vJbNIWvt3p8xUytdb9nK2yEDCKbkaXPtMD7T6TMqK6mM0KewwG7WStMEE6hoxZ+QqYs1j9OyGZzGlxkuxsNxkeYIqJh5gk5b9+N+POZRziOXHljLT14dEg1xRlBtc6RYTyIonevHmba2cXh8X4kWXRoEctPjlLWh18PhuaHYz9369jfmJhbI/Q8VWc3YoimLtO/pilxfRCI3VNVNQVsDENY7JAN//u5lPNm7CxTueXk38WP1UL7a90I/H+8XR0/0oJ7URstR/gpzVrz2YwWlzkdqp3BJOZkuX5cz1Uh9G45bqcPxteXOsj2ND3PCItP1deGwi7V2mVfle6hRBcdBhLAx8DV7KvOB00fPlqjDyFobHDcfdxd1hBmbh+gYyULnw5oV82vdTh+dcjbblYczyZdbH7m3akL9ihVOudOasWZx86GFSHpW61hoPDzTejsbneU/5xYzLPk6aux9P/l2Nboqnebb+XhPEJ3ZVi6d22TrjAHzWCT4wF7fELyDaK5z2Ie1YdaJ6I7/rRA4FpeWcyCpySkGsCxjNAUN/Dx2jO0ew7fl+PHdjs0r3bRvhR4CnnmnDWtK3aQh3dDx3nn9SbhKvb5WtDDT6DEbOlMYlJT+FNsFtmNRe6htphdbhhjGk4R3cH/0Baadl1sT8HbtoM3UFC3emEOCpx82tCISCyeBLzPNL+WrjMdx1WtyL+tLD4w3c9V7ccSKWUPNkoN3yO3ngx0GczJSGtzTrOFpN5Tco+yDn8D+G83TaGj4J8GPeYXNdhjDQPlLGkkZ1inB6fXiAB/V93ThTRYWvyVxVvd9k/u5vqbyoD2RG2Xs7Zd+G2xrfRoxfDBtPbaTQUEi3H2wNd4pOjAVg7oHP+Cn1MdzDv2eX5mGiPcsIWT6BJzq50da0jw2lsYz5W0+Kixy7ojLbZGnV/jR6TF/DmgPpxJ/KwK/hT3hEOjauyTUdIipmM3teHsgtnaRhLC+IIzDzLb6841a+GeOoX1Xn0WguOJPogg5/yY5cC0y5bgqbRm2ypjbZ80HvD5g1cBZNApoQ6RPJO73eIdxLfsmyjw+17qeLjMRv9CiifvoRnyGDMebkYDhxgvT33qM4Pp7i3bvJ+VZG/osTpe9Y6+eHS5BM3wt+4gncWrbE+/hhonNP0TT7OOWNm7P2YAYpOVU0ALfEDxQj2OdjL3lS5jMX58J7dkZv9etwahei7Ri6hHblSO4Rp+W0hcRTZ7nt8820fGU5Pd9Z4+ASqCvkFRtQFPDz0COEIMTHrcp9A71c2flif+7u2pCvx3Zi+u1tqtwXpL942KJhVqPp4pMAwsCsDYc5mHOQdiHtrP53rdDavU6h42ur+XBJKUkn5TL6VKktJz7E25W0IunLNhlsK8dig5HYEC8OpuXT//11TDfcxfR06dowCcEWYzaDXf7luIsLrv/OoSu2ytjWLichV04G8svyifKJcsiuut3DtgIV2mL6N5PZX0IIPhzZlpEdI5gzrhPz7u/CiA7h1Pd1o6C0nPbTVlpXLyCzUX7eKc+zVWnGtmY3sOroEsfqSzN5ZXk8s+4ZAO5reR86jY5hMcPYn72fyettGk7G4giMhTKlU6N3TNs1TG8k9e6XPIWbqYjVpvZsOpLFvC3HKDQUWrOn7Bdk4+ZuR+e3HaPHLodjhbnI3PQs/R/4uLugd5P+fl3eTax9aggDml9GeYkrhKvKyAOVGngAdxd3h+KFQVGDmDtoLve1eABTaX1mtBpK9vC7EEIQ+soruLdujWuczI1Pf+99smZ9RfIdI0keNRpjrjTExkz544389ls8u19P9OI/CXroQdxatsBj724+W/sB9YpzCeki/YOH0vIpMRhJPHXWIQBYsXWYE78/4thvdv10QIGoHgyNHYpJMTmo1QFsOZrF0oRUFmx3LHSpzE9amxSVlTPscykO5+95frOZC2lefvTsUYe/9QGb0Qet5o3lOzCYDER4RxDgJlNaB0TZ9MEzC2wzacXohbEkDK2nzZ/s76mxNn1565be1hz9/s3q0TbCn23HsjmSXkAO3rQtLaNJqc2PDzA5RN5Yhmo2896INnQR+/nD5Vn4sCUoCoWGQrz13lwXJvXCH8g9yyvdp/HudV9hONsaoS0hIdN20xnWrgFv396aPk1C6B4XhJtOS6co+b6yC8sYOXML+1Pz+O6fZPq8u5Znf7G99v6S/TwREsSSLe84fX5zEudQYJAVmZZ88Lub302AW4C12hOg6PiDhHi7UpT8oNMxsi36TuaMmmOKdL/tKZlN1++7suFwOoNb1be66iwIF1vcpSxbrhi88kdTmi4lTn4/8juzDr4OipapN/Z2Oq+K5Koz8hdCPc96PNHxMaICvVgU0wPd+EccnndtLIN8Ffur2uPWpjX68AYIIXCNlWl03v0cc9N92svqt8yCMkbN3MJNn2yk8xt/k19iDtq6ekNz22qC4bMdxaYOLoXYAfByjq31masvNOhAA68GRPlEsTvd5lM1GE2MmrmFCfN3MW+Lo5HfcDiD3CJHg1ObvLn0AMfNefGtw6vIEroIDmRL/Z43e7xp9bdrdNloPaV+iL+bP24ubvx1219Mvc6WImhxcwxrK4Oq5QVxaD2O06eZD9fHBjKgY441vXJ463Z8d38Xdr00gE/vbMf/9bWv1BQkenSma2YnXqtvu4lYHIB6YaBP0xB+dLXzI2clkW/Ix1PnaZ2YaBXAN4InvsuhNGMgKC58/K9N06gyOjT0p3usTSJj8q8JvLRor6NIm7C5Sianr7eV2CMbY1gCvwKBRkhzIYSwrn661O9Co7JnaRcRzLYX+rPkoXvx0crK3BsiZR75BwGO43paka9NzFspj+dSwF1dGhLu75gl1TDYTqgvYwCPRv3GmVxo7DYUV60rL29+mTJjKbc3uZVhbWteiOxq4Zo28hb6N5NLvHo+jrIHLoGBskgJ8OjaFZ+bnXNYvXv3dtrm1aMH0UuX4mtWlwzuIguT0vNKrDngAN3fXmN70W2zYOR8aHe31JeJtBMl8o2QUXiNRpY9A4ddm/Pd9tPc/sVm/LVNWJuyll8O/cK+03k8u9BRHKtHXBB3d23I9NtbU2IwseXopZnNF5cZmfrnPoeUuXNhCWKCLd+9Jskqlq6DPhF9eKTtI3jrvdH57sE9TPq2LXUW4d7h1tZwW49mMfQzuboY3TmScH93mvp0RggTOV4zeXiQgSUn5wLwWb/PrMYvwFOPm06Lv6eelU/0ZHTnSLa/0J9V7T/j07zRrN8ZxuQs+dlnIbNkosQZ/Nx1JLr78JmfL2laLUrqbk6fzcGjrIxB6z9jakYW487mgUcARWVGFEMQ+tLWpBY4BiQrw5J2CqCtsABa9WQvpo1yDPadfTvcGlR9boMtmcBevwngze5v8n7v92msPE18kr81XbJpfR9+uXUuj7d/nAfajAVgiZcnRXaSwcU4uuN0vjvZV7iS2HpuRAd78s19ndk9pRdnFBkYf6HLC8QEBvPmXwc5lVvM6M4NebStbCjSJrgNL3R54Zyfw7WMauSByTc2Zclj3WkY6FxIE/zY/6Hx8CD40Yk0eGc6cf9sdnjed+hQp9cAuEY3InTaVJrE78HT3RUPvZZ3V8jlfgM/OWM5W2ywVQO6uEKzm2Dop7IZiqUo4qYP4IlEm25JVHcA3sy8npcW7WXH8Rw2J8jZ2pR/pnDvnG38avZxNq0vf3htI/yYNqwlw9o2wF2nZcvRSqQO/iNZdm7HqwxiAAAbCklEQVSN33ef4utNxxzyvc+FJU4xZ2ynGrsmewoMBQgE7i7yM7d03bLg7+bcL/TXXbYK2zYRfmx8ti+f3T4cF7xJKtjNhL8ncCjnEFqhpXuD7pWeN66eN2/e1opgb1cCveTkYX1eKGPyChiZl88ZHXxh6k+4WxqJx5Zxf4gPM/x96R/ZgOO7/6a0KIXAY+vQJG/k1oJCPPq9QupZmx7SwCZNSCtKO6c+vhCCx/tJt6NlxQTQu0kwIX4mtmfJVaomS+aV3xweRt+fpYidQN4VtEJLkwBHRccY3zj6RvTn87VSm8d+Fl7fsz7jW42niZ2m09Lm/WHo5/BSJkffGAzYEgBcQ5bz0e43+CdtJUN67eTLw4/T40dbQHdU01GEmX8zQsCYLg0Z23Ise+7Zw3c3fndZCp+uZFQjj6yUbBFWeZWad79+NN66BY+OUovCxd+fZgf20+DDDwh69FF0YVXrsQut1trNyT4H/K3hrXh3hAwYVqml0qgnPLoDOoxz3O4XwdMt1rPaJP38LwxuhrdJuoMURZCRX4RwyWXhw92sKnyWQKbeRUObCF/mbk7m/rnbSUg5y9KEVGvl6IXy+7+n6PDaKuJT5OpkR7KcwcennGVz0rmPqSgKuUUGJvSOoU/TEJYlL+PhVQ87Fg5dJGcKz+Ch87DOtj/qYyuaMeS1ItzTOTPF31OO2bShLXDTyZluhL8n3wye4bCfEDYXRnUEe8njZeDHemMrOoYOAo2RkuZ+9GkQyJiN/6PIrmjNLflHirQm/Cy5653Gk976Ebq9KRUMn7uxKe1CYyk1lnKq4Nyt/Z4Y0Jh+TUPIKpRuumnDWvLRyHY8ufZJ1pyUq8miMvk9zjH7z/dn7SenVI7nNzd+43A8o0mh1ztrGfaZrdFOZCUTJM6mcH+uXKm5uvnKuhCtDo1GcH3LfKfdFx5ayNy9cx1iDa90k4Jxfh7yM7Rf7WmE5oLiM9cqqpE/D4TOOSDoM2gQwY+ev2TA1KG2picxwV5W19ALvyWSnl/CkI83WPODrQTFOVXAnS02sDQhlRta1OPzMe25u1tDWoT6UZo+ECEUXOsvwivuLValzySvPAUwEhlgK8mP8JeP/z50gps/X8WE+bt4fEHVOdJV8cqiRCb9KF/36p/7UBSFk3bZQ8cyz90o+Uh6AeUmBT+z1swz655h06lNbE3deo5Xnh+5JbksSlrk0LS5b2RfFg1dxJOxv1Jyagxni51TSrMKSqnv48bd3aIctlesv6iux4E9kQE2A3iP4Tk63PAhgW6BzC9zzBxBkQb2sF6HUQj8Lemurj5sT7a5wCIDPGgTLCcJ9l3MqsNyo/d2deGuLpH4euhIyLAZU1OpY1bKHYulTtOMM+nWc1nYfTKXU7nFVlfbkFah3Ny6kuYeWUe466w05hUbZzeNSQKTK+WFsqK7W2g3B+MOEH9PPLc3ljK7rma10VDfqjOvVCpHNfKXiQ4NA4gNkbOQUF83q7HdeCSTGWuPsvd0nlXrpjo2H8mkqMzI+B7RDG4ViptOy4Q+MZgM0u2g95ct6+bvn88Zn2nog9YQZvfDqG9+7BX3Gl6NZaCxoLSSloLVUFpuZEHye9Zsk53Hc1gcn8qpHJs74Vw68FuOZjHgA+lzdXXROhiBHWk7Luh6AMpN5Q6iWauOr2JZ8rJK9432iyYqUBrs6XYVlhbS80sJ8XGWpbavpP7uxu/Ou7Vc8zAfh5t8kJcrBpOBUsVRbz7OZRQA+82rPz+jeUWjcWHdoXR0WsGvE65jUMv6xPrF4qXzqtLIr0hewZTNU8gry2N3+m5W5j+K1vMwkYEeCCHILM6kqLyIALcAHmr9EK8NGoIhryW3lDi6ProUO680D1UosLq7W8PKZ9Qp2/Eyu3xe3/q69aay8dRGfjr0Ey2CmvPLbV+xbPgyqyBcPY96vNPzHWYPnO1wzKQMGRAe06UhKheG6sy6jPz0UDcy8ksRQjj4ML/eJJs2nKs8P6ewjEfMuiRtI2wZC9fFBPHKjdczPf5Hp9c0jSi03lziM+IJ85ezSqGRBmZinxg+W5PEsmMrMJhKaRvclk/+/YSp10/FzaXyWdPhrNPo/Xag99vBDR7zWLgzhSl/7CWrsIxnbmjCO8sPUmKouuhq7+mzjJq5BY3rGdwjZ5Fa9Bb7s2zunVkJs3ig9QNWP/r58GX8l8zYM4PVI1ZTaCjkibXO2i72WPRuFu5M4fF+cUTYrXbS80tp4Of83r31NmXDtiEX1hv3nm5RDG3bgPS8EoSQMQJL559GZQaO6XX0a9iPw0fns85DXttWZQg38zOm/DR2ncilV+MQawGUxU++/cx2EjMTrdLIFp5aJzVq7KWO3er/wsfDxrMzbSe/Hpb9g2f0n0GzwGak5BRR8ttdaLXLeSLoV7oUlxBjKJcGInkjRHWn3Ghiw+FMkrNsN2QfVw0d6lcwI1u+gANLwLs+rj42Dfw7l97J3c3v5rt9ss4kyi+M5vVlPGlMszHkl+VzT4t7CHJ37gb14pBmfLP5uLU2QOX8UWfyl5EATz1NzMFQF62Gh3o6pn1tPIdvfIdd1opO6zh0fWNsP/Jwzyjr4xZhgQghSC9KZ8zSMSSWfs28B237NvDzQOgyeWb9Uzy/8Xmm/DOFv5L/YvuZqpURd6XKGZlW6Hh3RBsaBXla/b29mwTTNVrmZxeWVt4ZaePhTDT6dDyjP0TjUsj8U//H53tkxaXFNbD51OZKX1sZ+WX5zNgj/eUbT220HsvC0x2dtYnC/W1G3aJmuPFwJrlFZaTnlRDs7WzktRotT3Z4kgVDFjg9dz74uuuIM2ehzBo4iyHRQ/j29Bm+PpPGVwO/IsY/HEXREO/migY9h4pl3OXtBA9Sc4sJ8nJsMejn6kdyXjKjl4xm3cl11u1Hcx1rAyxo9Lm4uGYxdtlYq9hYrH+s9fPQazX8YbyO+87m06LMgJt5Fs6qKQAsSUhl3NztfLnuKDHBniyb1IP42FnoPmkr5RosLJssG1Yf34zwcWxKYzHwAEFuNmPuofPgyY5PVmrgQa6EPx7dDhetarIulIv6xIQQ7wghDggh4oUQvwkh/Oyee04IcUQIcVAIccPFX+rVx3ODmzHITpdl67Fsa0epM2dL+Hu/ozpgTjX57Q18ghnZZCQAw5vYMn6O5B4BbAJT289sx83DJgnbKdodr1iboNq2M9LdU2as/FylxlLe2SM7CrlppSG0+N/fHt6KFmG+3NFRBjPXHcrgjz2OlbiKovDN5mS0HskO27ef2Y5AWDtwWWa5laEoClnFWYxfMZ7Xt7zO0qO2ZuYvb37ZoTAs3Cuce1vc63QMN52W/VMH0bGhP4v2nGbm+iTumr2VtlNXklVYVqWy5biW4xyayv9XGvk24q0eb9Fu+DyCxq+jS2gXBjSvj79LFADtArtzyrUpXUo+5cuC7hSWGZ1Weva9Rfdk7OHNrW/S6ptWDF0kx39Uk1EMjZGPLTdP+88KpAa6hV8euQ7vgBDGlv2PD9sugZezpTJiofy+ZNoJs7WJ8KNpiBck/Q3F2RD/I+z5UXY4spB3CjwCebnbywyLHcY9ze9xOPdDbZwVO1Vqnot116wEnlMUpVwI8TbwHPCsEKI5MApoAYQBq4QQjRWlGkWma5QejYNYttf2Y31n2QFigr14b6X0dy95rLs18yfFHJhdPqlnpcd6pM0jdKzXkf4N+5NZnEmpsZSFhxZyquAUr/4je8CmFqYybrktYyervPL2dOnFlTeFsIh6ARSW5zv4wW9pI2dt9cxBPovkbVm5iZtah3L37K1kFZZx+mwJw3qG8neFHtL1POtR31Pe9CxVlhaWHF1CXlkeI5uM5OvEr/lol8ySOVeQtjo1Qne9ls6NAvh8bRJvLLU1Pe8eK+sKLguNbfMfrUaw6PY5bDq1id4RvTnZXmHwx7YZsk8FI98yqCV7s/biIlyYlTDL4Tl/V3+e6fQMCw8tZFHSIsI8w9ibtZeNp6SC46gmoxgSPcThNa3Cfdnwv750mGZk7ZazPHqzgkvrkbLJR34aZ+xSOGOCvRwlOP6U1bBkmquCPYJkx6kGHRjReAQjGo/gYPZBvt33LQB3Nr3Twf2lcum4KCOvKIp9KegWwNJxdiiwQFGUUuCYEOII0Bn452LOdzUyulMkHnotN7UO44OVh6x5xxY+WHmY8T0aMWqmTYfb4vKpSKB7IIMaSbniyZ0nk5CRwMJDCxm3bJxTdoMFS9Nzfc5IbuliZOGhhUDVnX9WJq9CMbrSxetRthW/x/6s/fz8cDeS0gtw18vskJYV0lGf/nkPM9cncSjNZrhDAwyQAR/2+ZCXN71MXlke5aZyPHUyZmBv5MtN5UzeIHVSEjIS+POoc7elpgFNebvH22QUZ9DItxEHsg/w1NqnGBZbvVhVw0Cb26ZpfW9iQrx4e3hrvC5hE+jqCHAL4OYYWSPRPAyS3xpC1OQlgLORf6bTM4xqMooVx1c4NSYZFjsMvVZvbWZypugM9TzqEZ8pC+UeaP0AIR6V+7ctrrc/409za4RZl33rF6Tk2IoBu0YHVq5Df8gc7L7jG9C5Q5jtJmvpzQzQK/wcUh4qNUZNfpPvAyyRvwZIo28hxbzNCSHEg8CDAJGRkZXtclWj0QhubSeF0ib0iSUjv5Sfd9paza3an8aq/edu6lAZlt6zlo5H9za/l9vibmNX+i66N+jOgIUDWJS0CBBkZ8RyY2g/juYeZVf6rkqNfJGhiL+Sl2IsjqVn8y5sOwiHcw8zvlUvq04KgK+HDjedxiH4am/go4J0zDs4E63Q0i+yH0uOLmHl8ZX0Cu+Fi8YFdxd3CstsN6XEzETrY4uB796gO946b7ae2Up2STb3NL+HaL9oov1knCPEI4Ttd52745KlAM7b1YVlVayQ6grNK3Qqc9W6EusfS6x/LN/u+5ZCQyE3Rd/EjY1upH2INK7tQtoxoOEAxrUYxzPrpdBY6+DWVRp4gLHXRTF3czIJKXnc2sqcPrnxAw5592Zg83q8Nbw1AZ56WPG+84vTzGMVGAfejmmZQggS7k3AYDI4uIlULi3n9MkLIVYJIRIr+TfUbp8XgHJgvmVTJYeqtDRPUZSZiqJ0VBSlY3DwpRHNv1LwcnXhnRFtGHtdFM8Oasqel53zsN+qRDu8Klw0LrzR/Q3r31G+UUT7RXN749sd1A0fbTEVxejNHTO2WQtf/kj6w9oc3cKK43Lhpih6GgUEEeoZyuGcw5We+y5zqtvUoS2s4l3dogPZ8/JAXhohf+CWRi4vdn2ROTfMsRa+eLh4kFWSZY0jVEypnHb9NL7o/wXTe01n3ch1rBi+wtpv80Kx1BB41tLM/Xx47samTB/emg4NnatzLWwYuYH7W97PxLYT6RneEy+9zKjSaXW83/t9WgW34uVuLzO40WBmDZhV5XEAptzSAj8PHV9vOoZB6KwFeU+cfYuBLrukgQfIOCgVVIMcq2Flw/GqbyKqgb+8nPObrShK/+qeF0LcC9wE9FNsNdYpgH0pYThQuRauihNTbrEF9h7uFcOMddKFc2PL+ozqfGGrHftClsGNBlsf2+cg39P6Jt5cKDsXHc8qxFgcjtY9hX/T/6VvZF/rfpZGFqVnhhIb4kWsX6w1sFuR/w1qyuP94/B203FLmzBmrj9K4/AijJp8ckulfsvn/WQWTIBbAAH1bSuBIPcgFh9dzOKji9kwcgOpBan4ufrRIrAFPnofJxdMqFclhTjnSaivG+O7N+LW9pUuNOsED/WqogWkHTqtjkkdJlW7z3Vh11lVLc9FbpEMoO5IzqFbRBfYOYebNP/AoX8wL7wh5xg0Hggj58HyF2SVdkgzmWmjVqLWGS42u2YQ8Cxwi6Io9uWafwCjhBCuQohGQByw7WLOda3y7KAm/PNcXxr4uTOxT+y5X1CBSJ9IZvSfwdY7tzo0owYpOjVv8Dzc9S5W7Zhe76yl6PiDKIogPiPeGth8f+f7zN8/H52pHq3qRxLm5064dziHcg45KGBa0Lto8HaTMzY/Dz3/G9SUl3bey/A/hnM87zgCQdewrk6vA7g1ziZmNXfvXNKL0wnxCGHGgBlM7zX9gj+D6hBC8OJNzauUtbhWGXtdFCAbzjiI5VkoyYPsYxBo/k7e8LoMIvtFQnAT5/1Vao2LTTr9FPAGVgohdgshZgAoirIX+AnYBywDJqqZNf8NIQShvu5smtyXlg3+myG6vsH1TgYeZLDSMtO3uFQAUPSYSusxO3E241eMZ93JdcxJnENReRGFOU3pGSfdapYg6d1/3V3t+ecmzuWm36Q7JbM4kzl756CgVLls71ivo/Xx3qy9pBakEux+bbvyLjcWUbN3lh+EgEaYtHYVwIsmyp4GJoNUTFWp01xsdk2VU0tFUV4HXr+Y46tcPly0Gno1DmbdIZnXaCppgNZNpnY+v9FWvm8sDbHq7lSVsTNu2TgGRQ1iZNORZJdkW1vH2TOl25QqryXGz+ae2JIq4/eDowdXtbvKJcCiJwRQUFrO4Yi7aZdsbir+7zzbjr7nbr+oUruo5WMqVl4c0ozeTYL5emxHjCU2H7VDYZJJR4CnNPIW37j9LLu4vJgdaTt4betrxGfEc+9fzoVIAMMbD6/yOlw0LkzuPNmaAaIVWobHVb2/Ss0jhGDWPXJFdd/c7UxKG8zbPpXotntUXqGqUndQjbyKlbh63swd15lWDfwwFslURHutbpPBh/KCpni7yW3NA5szJHoIJcYSigwyJPPTwZ+s+49ZOobkvGQAFgxZwKbRm3B3ceeh1ueudBzTbIxVgbC+Z30nBUiVS4+lEci2Y9kczykhrved8PQR2djGgqta0FTXUY28ihNBXno8aUBp+kBm9p3HDQ1lVWZxyl2g6B2KsfpF9iO/LJ/EzEQMJgPv7njX6Xgrb19JiyCZGbN59GYebffo+V2HWcfER+9zjj1VLgUV2/F1bhQAXsGysY3l5q9m0dR56m5ysEqtIYTg4zvbM26OEQyhRDOe4pRATCURzB3XySpbAFgrKh9e9TAGk0y7e6vHW6w7uY7C8kLe7vG2NWcbuKAuPpYG25YAr8rlRaMRDhW39uPOE/ugqOY6jKlcOlQjr1IpDc1FQiNmWJQoZGPxiiJZFr+5xcCHeobSL7Kfky7Kf0GvkUU3YV5Vd99SuXw4KJ9613OqaFWpm6hGXqVS7KV4Ad6/ow3HMgud0jjtZ9nPdnqW2+Juq1KH/kLpFtaNCW0mcGezO2vkeCr/jVVP9nLo5atyZaEaeZVK0btoeKhXNF+uO8qNLetzW/tzp8qNbDISnbbmStZdNC480vaRGjueyn8jNsTL2nhG5cpDDbyqVEm/pvXw0GvPKbt7S8wt+Lr61qiBV1FRqRmETW6m9unYsaOyY8eF9/dUuXQoilJ5/04VFZU6gxBip6IoHSt7Tp3Jq1SLauBVVK5sVCOvoqKichWjGnkVFRWVqxjVyKuoqKhcxahGXkVFReUqRjXyKioqKlcxqpFXUVFRuYpRjbyKiorKVUydKoYSQuQDB89zd1/gbA3sc6H71tZ+tXnuS/FegoDMWji3On6X95jnO87ne8yr6bOpyXM3URSlcnF/RVHqzD9gxwXsO7Mm9rnQfWtrvyvhGi/wvZzXWNf193I1jd8lOnet/KavkM+mxs5d3ed8Jbtr/qyhfS5039rarzbPfSney/lS19/L1TR+l+qYNXnuq+mzuRTndqKuuWt2KFXoL6hcXahjfW2gjvPlobrPua7N5GfW9gWoXDbUsb42UMf58lDl51ynZvIqKioqKjVLXZvJX/UIIQrO8fxaIYS6vL3CUcf52uBKGGfVyKuoqKhcxdSKkT/X3e9qRwjRWwix2O7vT4UQY2vxki4Z1/JYq+N8bVDXx1mdyauoqKhcxdSakRdCeAkh/hZC7BJCJAghhpq3Rwkh9gshZgkh9gohVggh3GvrOlUuHnWsrw3Uca6b1OZMvgS4VVGU9kAf4D1h6zUXB3ymKEoLIBcYXkvXeKkox/Gzd6utC7lMXKtjrY6zOs61Tm0aeQG8IYSIB1YBDYB65ueOKYqy2/x4JxB1+S/vknIcaC6EcBVC+AL9avuCLjHX6lir46yOc63jUovnHgMEAx0URTEIIZKx3QFL7fYzAlfF0k4I4QKUKopyUgjxExAPHAb+rd0ru+RcU2OtjrM6zrV7ZY7UppH3BdLNX4Y+QMNavJbLRQsgCUBRlP8B/6u4g6IovS/zNV0OrrWxVsdZHWfM23tf5mty4rIbecvdD5gP/CmE2AHsBg5c7mu5nAghHgYeAybV9rVcLq7FsVbHWR3nusZllzUQQrQBZimK0vmynljlsqOO9bWBOs51m8saeDXf/X4AXryc51W5/KhjfW2gjnPdRxUoU1FRUbmKuaQzeSFEhBBijbkQYq8Q4nHz9gAhxEohxGHz//7m7WOEEPHmf5vNy0DLsQYJIQ4KIY4IISZfyutWuXBqeKy/FkKkCyESa+v9qFROTY1zVcdRuQScb/up//IPCAXamx97A4eA5sB0YLJ5+2TgbfPj6wB/8+Mbga3mx1pkFDsa0AN7gOaX8trVf7Uz1ua/ewLtgcTafl/qv0szzlUdp7bf39X475LO5BVFSVUUZZf5cT6wH1kgMRT4xrzbN8Aw8z6bFUXJMW/fAoSbH3cGjiiKclRRlDJggfkYKnWEGhxrFEVZD2RfpktXuQBqapyrOY5KDXPZAq9CiCigHbAVqKcoSirIwQZCKnnJ/cBf5scNgJN2z6WgfiHqLBc51ipXCDU1zhWOo1LDXJY8eSGEF/ALMElRlDybnEWV+/dBfiG6WzZVspsaMa6D1MBYq1wB1NQ4VzzOJbrca5pLPpMXQuiQgzhfUZRfzZvThBCh5udDgXS7/VsDXwFDFUXJMm9OASLsDhsOnL7U165yYdTQWKvUcWpqnKs4jkoNc6mzawQwG9ivKMr7dk/9AdxrfnwvsMi8fyTwK3C3oiiH7PbfDsQJIRoJIfTAKPMxVOoINTjWKnWYmhrnao6jUsNc0jx5IUR3YAOQAJjMm59H+t5+AiKBE8AIRVGyhRBfISVIj5v3LVcUpaP5WIOBD5GZNl8rivL6JbtwlQumhsf6B6A3EASkAa8oijL7Mr0VlWqoqXGu6jiKoiy9PO/k2kEthlJRUVG5ilHb/6moqKhcxahGXkVFReUqRjXyKioqKlcxqpFXUVFRuYpRjbyKiorKVYxq5FVUVFSuYlQjr6KionIVoxp5FRUVlauY/we1OM8G+gLBvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
